def __init__(self,
func: callable,
annotations: dict,
strict: bool = True,
wrap: (bool, callable) = False,
name: str = None,
desc: str = None):
self.func = func
self.annotations = ann = self.make_annotations(annotations)
if strict:
self.plac_func = plac.annotations(**ann)(func)
else:
self.plac_func = plac_annotations(wrap, **ann)(func)
if name is None:
name = func.__name__
self.name = name
if desc is None:
if func.__doc__ is not None and func.__doc__:
desc = func.__doc__
else:
desc = f'command `{name}` (no description)'
self.desc = desc
self.plac_func.__doc__ = desc
def annotate(*args, **kwargs):
'''Return a decorator for plac-style argument annotations.'''
return plac.annotations(*args, **kwargs)
d={}
for item in patterns.A.iter(x):
val=((item[0]-len(item[1][1]))+1,item[0]+1,item[1][0])
list_ent.append(val)
d["entities"]=list_ent
TRAIN_DATA.append((x,d)
df_test=pd.read_csv('/Users/pragnyasuresh/Desktop/team_no_8/test_ner.csv')
test=df_test["review"]
TEST_DATA=list(test)
@plac.annotations(
model=("Model name. Defaults to blank 'en' model.", "option", "m", str),
output_dir=("/Users/pragnyasuresh/Desktop/team_no_8", "option", "o", Path),
n_iter=("Number of training iterations", "option", "n", int),
)
def main(model=None, output_dir="/Users/pragnyasuresh/Desktop/team_no_8", n_iter=100):
"""Load the model, set up the pipeline and train the entity recognizer."""
if model is not None:
nlp = spacy.load(model) # load existing spaCy model
print("Loaded model '%s'" % model)
else:
nlp = spacy.blank("en") # create blank Language class
print("Created blank 'en' model")
# create the built-in pipeline components and add them to the pipeline
# nlp.create_pipe works for built-ins that are registered with spaCy
if "ner" not in nlp.pipe_names:
ner = nlp.create_pipe("ner")
def overlap_tokens(doc, other_doc):
"""Get the tokens from the original Doc that are also in the comparison Doc.
"""
overlap = []
other_tokens = [token.text for token in other_doc]
for token in doc:
if token.text in other_tokens:
overlap.append(token)
return overlap
if __name__ == '__main__':
plac.call(main)
@plac.annotations(
model=("Model name. Defaults to blank 'en' model.", "option", "m", str),
output_dir=("Optional output directory", "option", "o", Path),
n_iter=("Number of training iterations", "option", "n", int))
def main(model=None, output_dir=None, n_iter=100):
"""Load the model, set up the pipeline and train the entity recognizer."""
if model is not None:
nlp = spacy.load(model) # load existing spaCy model
print("Loaded model '%s'" % model)
else:
nlp = spacy.blank('en') # create blank Language class
print("Created blank 'en' model")
# create the built-in pipeline components and add them to the pipeline
# nlp.create_pipe works for built-ins that are registered with spaCy
if 'ner' not in nlp.pipe_names:
ner = nlp.create_pipe('ner')
nlp.add_pipe(ner, last=True)
def annotate(*args, **kwargs):
'''Return a decorator for plac-style argument annotations.'''
return plac.annotations(*args, **kwargs)